Neuro schedulers for flexible manufacturing systems

In this paper a dynamic neural network (DNN)-based controller is constructed to provide the basis upon which a scheduler is developed to guarantee that system production will reach the required demand while satisfying buffer capacity constraints. Lyapunov stability theory is used to prove boundedness of all signals in the closed loop.

[1]  J J Hopfield,et al.  Neurons with graded response have collective computational properties like those of two-state neurons. , 1984, Proceedings of the National Academy of Sciences of the United States of America.

[2]  P. R. Kumar,et al.  Dynamic instabilities and stabilization methods in distributed real-time scheduling of manufacturing systems , 1990 .

[3]  Manolis A. Christodoulou,et al.  Adaptive control of unknown plants using dynamical neural networks , 1994, IEEE Trans. Syst. Man Cybern..

[4]  Gerald W. Evans,et al.  A review of multi-criterion approaches to FMS scheduling problems , 1991 .

[5]  K. Preston White,et al.  A recent survey of production scheduling , 1988, IEEE Trans. Syst. Man Cybern..

[6]  Marios M. Polycarpou,et al.  High-order neural network structures for identification of dynamical systems , 1995, IEEE Trans. Neural Networks.

[7]  A. Sharifnia.  Stability and performance of distributed production control methods based on continuous-flow models , 1994 .

[8]  George A. Rovithakis,et al.  Direct adaptive regulation of unknown nonlinear dynamical systems via dynamic neural networks , 1995, IEEE Transactions on Systems, Man, and Cybernetics.

[9]  C. R. Bector,et al.  A review of scheduling rules in flexible manufacturing systems , 1989 .

[10]  Bir Bhanu,et al.  Adaptive image segmentation using a genetic algorithm , 1989, IEEE Transactions on Systems, Man, and Cybernetics.

[11]  Manolis A. Christodoulou,et al.  Neural adaptive regulation of unknown nonlinear dynamical systems , 1997, IEEE Trans. Syst. Man Cybern. Part B.

[12]  Andrew Kusiak,et al.  Designing expert systems for scheduling automated manufacturing , 1987 .

[13]  S. Gershwin,et al.  A control perspective on recent trends in manufacturing systems , 1986, IEEE Control Systems Magazine.

[14]  Lawrence Davis,et al.  Job Shop Scheduling with Genetic Algorithms , 1985, ICGA.

[15]  R. Palmer,et al.  Introduction to the theory of neural computation , 1994, The advanced book program.

[16]  M. Dolinska,et al.  Dynamic control of flexible manufacturing systems , 1995 .

[17]  Manolis A. Christodoulou,et al.  A recurrent neural network model to describe manufacturing cell dynamics , 1996, Proceedings of 35th IEEE Conference on Decision and Control.